def print_aff_info(filename, verbose=False, *args):
b = affopen(filename, 'r')
indices = [int(args.pop())] if args else range(len(b))
print('{0} (tag: "{1}", {2})'.format(filename, b.get_tag(),
plural(len(b), 'item')))
for i in indices:
print('item #{0}:'.format(i))
print(b[i].tostr(verbose))
def set_positions(self, atoms=None):
"""Update the positions of the atoms and initialize wave functions."""
spos_ac = self.initialize_positions(atoms)
nlcao, nrand = self.wfs.initialize(self.density, self.hamiltonian,
spos_ac)
if nlcao + nrand:
self.log('Creating initial wave functions:')
if nlcao:
self.log(' ', plural(nlcao, 'band'), 'from LCAO basis set')
if nrand:
self.log(' ', plural(nrand, 'band'), 'from random numbers')
self.log()
self.wfs.eigensolver.reset()
self.scf.reset()
print_positions(self.atoms, self.log)
def print_ulm_info(filename, index=None, verbose=False):
b = ulmopen(filename, 'r')
if index is None:
indices = range(len(b))
else:
indices = [index]
print('{0} (tag: "{1}", {2})'.format(filename, b.get_tag(),
plural(len(b), 'item')))
for i in indices:
print('item #{0}:'.format(i))
print(b[i].tostr(verbose))
def print_ulm_info(filename, index=None, verbose=False):
b = ulmopen(filename, 'r')
if index is None:
indices = range(len(b))
else:
indices = [index]
print('{0} (tag: "{1}", {2})'.format(filename, b.get_tag(),
plural(len(b), 'item')))
for i in indices:
print('item #{0}:'.format(i))
print(b[i].tostr(verbose))
def main(args):
verbosity = 1 - args.quiet + args.verbose
query = ','.join(args.query)
if args.sort.endswith('-'):
# Allow using "key-" instead of "-key" for reverse sorting
args.sort = '-' + args.sort[:-1]
if query.isdigit():
query = int(query)
add_key_value_pairs = {}
if args.add_key_value_pairs:
for pair in args.add_key_value_pairs.split(','):
key, value = pair.split('=')
add_key_value_pairs[key] = convert_str_to_int_float_or_str(value)
if args.delete_keys:
delete_keys = args.delete_keys.split(',')
else:
delete_keys = []
db = connect(args.database, use_lock_file=not args.no_lock_file)
def out(*args):
if verbosity > 0:
print(*args)
if args.analyse:
db.analyse()
return
if args.show_keys:
keys = defaultdict(int)
for row in db.select(query):
for key in row._keys:
keys[key] += 1
n = max(len(key) for key in keys) + 1
for key, number in keys.items():
print('{:{}} {}'.format(key + ':', n, number))
return
if args.show_values:
keys = args.show_values.split(',')
values = {key: defaultdict(int) for key in keys}
numbers = set()
for row in db.select(query):
kvp = row.key_value_pairs
for key in keys:
value = kvp.get(key)
if value is not None:
values[key][value] += 1
if not isinstance(value, str):
numbers.add(key)
n = max(len(key) for key in keys) + 1
for key in keys:
vals = values[key]
if key in numbers:
print('{:{}} [{}..{}]'.format(key + ':', n, min(vals),
max(vals)))
else:
print('{:{}} {}'.format(
key + ':', n,
', '.join('{}({})'.format(v, n) for v, n in vals.items())))
return
if args.add_from_file:
filename = args.add_from_file
configs = ase.io.read(filename)
if not isinstance(configs, list):
configs = [configs]
for atoms in configs:
db.write(atoms, key_value_pairs=add_key_value_pairs)
out('Added ' + plural(len(configs), 'row'))
return
if args.count:
n = db.count(query)
print('%s' % plural(n, 'row'))
return
if args.explain:
for row in db.select(query,
explain=True,
verbosity=verbosity,
limit=args.limit,
offset=args.offset):
print(row['explain'])
return
if args.show_metadata:
print(json.dumps(db.metadata, sort_keys=True, indent=4))
return
if args.set_metadata:
with open(args.set_metadata) as fd:
db.metadata = json.load(fd)
return
if args.insert_into:
nkvp = 0
nrows = 0
with connect(args.insert_into,
use_lock_file=not args.no_lock_file) as db2:
for row in db.select(query, sort=args.sort):
kvp = row.get('key_value_pairs', {})
nkvp -= len(kvp)
kvp.update(add_key_value_pairs)
nkvp += len(kvp)
if args.unique:
row['unique_id'] = '%x' % randint(16**31, 16**32 - 1)
if args.strip_data:
db2.write(row.toatoms(), **kvp)
else:
db2.write(row, data=row.get('data'), **kvp)
nrows += 1
out('Added %s (%s updated)' %
(plural(nkvp, 'key-value pair'),
plural(len(add_key_value_pairs) * nrows - nkvp, 'pair')))
out('Inserted %s' % plural(nrows, 'row'))
return
if add_key_value_pairs or delete_keys:
ids = [row['id'] for row in db.select(query)]
M = 0
N = 0
with db:
for id in ids:
m, n = db.update(id,
delete_keys=delete_keys,
**add_key_value_pairs)
M += m
N += n
out('Added %s (%s updated)' %
(plural(M, 'key-value pair'),
plural(len(add_key_value_pairs) * len(ids) - M, 'pair')))
out('Removed', plural(N, 'key-value pair'))
return
if args.delete:
ids = [row['id'] for row in db.select(query)]
if ids and not args.yes:
msg = 'Delete %s? (yes/No): ' % plural(len(ids), 'row')
if input(msg).lower() != 'yes':
return
db.delete(ids)
out('Deleted %s' % plural(len(ids), 'row'))
return
if args.plot_data:
from ase.db.plot import dct2plot
dct2plot(db.get(query).data, args.plot_data)
return
if args.plot:
if ':' in args.plot:
tags, keys = args.plot.split(':')
tags = tags.split(',')
else:
tags = []
keys = args.plot
keys = keys.split(',')
plots = defaultdict(list)
X = {}
labels = []
for row in db.select(query, sort=args.sort, include_data=False):
name = ','.join(str(row[tag]) for tag in tags)
x = row.get(keys[0])
if x is not None:
if isinstance(x, basestring):
if x not in X:
X[x] = len(X)
labels.append(x)
x = X[x]
plots[name].append([x] + [row.get(key) for key in keys[1:]])
import matplotlib.pyplot as plt
for name, plot in plots.items():
xyy = zip(*plot)
x = xyy[0]
for y, key in zip(xyy[1:], keys[1:]):
plt.plot(x, y, label=name + ':' + key)
if X:
plt.xticks(range(len(labels)), labels, rotation=90)
plt.legend()
plt.show()
return
if args.json:
row = db.get(query)
db2 = connect(sys.stdout, 'json', use_lock_file=False)
kvp = row.get('key_value_pairs', {})
db2.write(row, data=row.get('data'), **kvp)
return
db.python = args.metadata_from_python_script
if args.long:
db.meta = process_metadata(db, html=args.open_web_browser)
row = db.get(query)
summary = Summary(row, db.meta)
summary.write()
return
if args.open_web_browser:
try:
import ase.db.app as app
except ImportError:
print('Please install Flask: pip install flask')
return
app.databases['default'] = db
app.initialize_databases()
app.app.run(host='0.0.0.0', debug=True)
return
if args.write_summary_files:
prefix = args.write_summary_files
db.meta = process_metadata(db, html=args.open_web_browser)
ukey = db.meta.get('unique_key', 'id')
for row in db.select(query):
uid = row.get(ukey)
summary = Summary(row,
db.meta,
prefix='{}-{}-'.format(prefix, uid))
return
columns = list(all_columns)
c = args.columns
if c and c.startswith('++'):
keys = set()
for row in db.select(query,
limit=args.limit,
offset=args.offset,
include_data=False):
keys.update(row._keys)
columns.extend(keys)
if c[2:3] == ',':
c = c[3:]
else:
c = ''
if c:
if c[0] == '+':
c = c[1:]
elif c[0] != '-':
columns = []
for col in c.split(','):
if col[0] == '-':
columns.remove(col[1:])
else:
columns.append(col.lstrip('+'))
table = Table(db, verbosity=verbosity, cut=args.cut)
table.select(query, columns, args.sort, args.limit, args.offset)
if args.csv:
table.write_csv()
else:
table.write(query)
def run(opts, args, verbosity):
filename = args.pop(0)
query = ','.join(args)
if query.isdigit():
query = int(query)
add_key_value_pairs = {}
if opts.add_key_value_pairs:
for pair in opts.add_key_value_pairs.split(','):
key, value = pair.split('=')
add_key_value_pairs[key] = convert_str_to_int_float_or_str(value)
if opts.delete_keys:
delete_keys = opts.delete_keys.split(',')
else:
delete_keys = []
con = connect(filename, use_lock_file=not opts.no_lock_file)
def out(*args):
if verbosity > 0:
print(*args)
if opts.analyse:
con.analyse()
return
if opts.add_from_file:
filename = opts.add_from_file
if ':' in filename:
calculator_name, filename = filename.split(':')
atoms = get_calculator(calculator_name)(filename).get_atoms()
else:
atoms = ase.io.read(filename)
con.write(atoms, key_value_pairs=add_key_value_pairs)
out('Added {0} from {1}'.format(atoms.get_chemical_formula(),
filename))
return
if opts.count:
n = con.count(query)
print('%s' % plural(n, 'row'))
return
if opts.explain:
for row in con.select(query, explain=True,
verbosity=verbosity,
limit=opts.limit, offset=opts.offset):
print(row['explain'])
return
if opts.insert_into:
nkvp = 0
nrows = 0
with connect(opts.insert_into,
use_lock_file=not opts.no_lock_file) as con2:
for row in con.select(query):
kvp = row.get('key_value_pairs', {})
nkvp -= len(kvp)
kvp.update(add_key_value_pairs)
nkvp += len(kvp)
if opts.unique:
row['unique_id'] = '%x' % randint(16**31, 16**32 - 1)
con2.write(row, data=row.get('data'), **kvp)
nrows += 1
out('Added %s (%s updated)' %
(plural(nkvp, 'key-value pair'),
plural(len(add_key_value_pairs) * nrows - nkvp, 'pair')))
out('Inserted %s' % plural(nrows, 'row'))
return
if add_key_value_pairs or delete_keys:
ids = [row['id'] for row in con.select(query)]
m, n = con.update(ids, delete_keys, **add_key_value_pairs)
out('Added %s (%s updated)' %
(plural(m, 'key-value pair'),
plural(len(add_key_value_pairs) * len(ids) - m, 'pair')))
out('Removed', plural(n, 'key-value pair'))
return
if opts.delete:
ids = [row['id'] for row in con.select(query)]
if ids and not opts.yes:
msg = 'Delete %s? (yes/No): ' % plural(len(ids), 'row')
if input(msg).lower() != 'yes':
return
con.delete(ids)
out('Deleted %s' % plural(len(ids), 'row'))
return
if opts.plot_data:
from ase.db.plot import dct2plot
dct2plot(con.get(query).data, opts.plot_data)
return
if opts.plot:
if ':' in opts.plot:
tags, keys = opts.plot.split(':')
tags = tags.split(',')
else:
tags = []
keys = opts.plot
keys = keys.split(',')
plots = collections.defaultdict(list)
X = {}
labels = []
for row in con.select(query, sort=opts.sort):
name = ','.join(str(row[tag]) for tag in tags)
x = row.get(keys[0])
if x is not None:
if isinstance(x, basestring):
if x not in X:
X[x] = len(X)
labels.append(x)
x = X[x]
plots[name].append([x] + [row.get(key) for key in keys[1:]])
import matplotlib.pyplot as plt
for name, plot in plots.items():
xyy = zip(*plot)
x = xyy[0]
for y, key in zip(xyy[1:], keys[1:]):
plt.plot(x, y, label=name + ':' + key)
if X:
plt.xticks(range(len(labels)), labels, rotation=90)
plt.legend()
plt.show()
return
if opts.long:
row = con.get(query)
summary = Summary(row)
summary.write()
elif opts.json:
row = con.get(query)
con2 = connect(sys.stdout, 'json', use_lock_file=False)
kvp = row.get('key_value_pairs', {})
con2.write(row, data=row.get('data'), **kvp)
else:
if opts.open_web_browser:
import ase.db.app as app
app.db = con
app.app.run(host='0.0.0.0', debug=True)
else:
columns = list(all_columns)
c = opts.columns
if c and c.startswith('++'):
keys = set()
for row in con.select(query,
limit=opts.limit, offset=opts.offset):
keys.update(row._keys)
columns.extend(keys)
if c[2:3] == ',':
c = c[3:]
else:
c = ''
if c:
if c[0] == '+':
c = c[1:]
elif c[0] != '-':
columns = []
for col in c.split(','):
if col[0] == '-':
columns.remove(col[1:])
else:
columns.append(col.lstrip('+'))
table = Table(con, verbosity, opts.cut)
table.select(query, columns, opts.sort, opts.limit, opts.offset)
if opts.csv:
table.write_csv()
else:
table.write(query)
def bulk(name, crystalstructure=None, a=None, c=None, covera=None, u=None,
orthorhombic=False, cubic=False):
"""Creating bulk systems.
Crystal structure and lattice constant(s) will be guessed if not
provided.
name: str
Chemical symbol or symbols as in 'MgO' or 'NaCl'.
crystalstructure: str
Must be one of sc, fcc, bcc, hcp, diamond, zincblende,
rocksalt, cesiumchloride, fluorite or wurtzite.
a: float
Lattice constant.
c: float
Lattice constant.
covera: float
c/a ratio used for hcp. Default is ideal ratio: sqrt(8/3).
u: float
Internal coordinate for Wurtzite structure.
orthorhombic: bool
Construct orthorhombic unit cell instead of primitive cell
which is the default.
cubic: bool
Construct cubic unit cell if possible.
"""
if covera is not None and c is not None:
raise ValueError("Don't specify both c and c/a!")
xref = None
ref = {}
if name in chemical_symbols:
Z = atomic_numbers[name]
ref = reference_states[Z]
if ref is not None:
xref = ref['symmetry']
structures = {'sc': 1, 'fcc': 1, 'bcc': 1, 'hcp': 1, 'diamond': 1,
'zincblende': 2, 'rocksalt':2, 'cesiumchloride':2,
'fluorite': 3, 'wurtzite': 2}
if crystalstructure is None:
crystalstructure = xref
if crystalstructure not in structures:
raise ValueError('No suitable reference data for bulk {}.'
' Reference data: {}'
.format(name, ref))
if crystalstructure not in structures:
raise ValueError('Unknown structure: {}.'
.format(crystalstructure))
# Check name:
n = len(string2symbols(name))
n0 = structures[crystalstructure]
if n != n0:
raise ValueError('Please specify {} for {} and not {}'
.format(plural(n0, 'atom'), crystalstructure, n))
if a is None:
if xref != crystalstructure:
raise ValueError('You need to specify the lattice constant.')
try:
a = ref['a']
except KeyError:
raise KeyError('No reference lattice parameter "a" for "{}"'
.format(name))
if crystalstructure in ['hcp', 'wurtzite']:
cubic = False
if c is not None:
covera = c / a
elif covera is None:
if xref == crystalstructure:
covera = ref['c/a']
else:
covera = sqrt(8 / 3)
if orthorhombic and crystalstructure != 'sc':
return _orthorhombic_bulk(name, crystalstructure, a, covera, u)
if cubic and crystalstructure in ['bcc', 'cesiumchloride']:
return _orthorhombic_bulk(name, crystalstructure, a, covera)
if cubic and crystalstructure != 'sc':
return _cubic_bulk(name, crystalstructure, a)
if crystalstructure == 'sc':
atoms = Atoms(name, cell=(a, a, a), pbc=True)
elif crystalstructure == 'fcc':
b = a / 2
atoms = Atoms(name, cell=[(0, b, b), (b, 0, b), (b, b, 0)], pbc=True)
elif crystalstructure == 'bcc':
b = a / 2
atoms = Atoms(name, cell=[(-b, b, b), (b, -b, b), (b, b, -b)],
pbc=True)
elif crystalstructure == 'hcp':
atoms = Atoms(2 * name,
scaled_positions=[(0, 0, 0),
(1 / 3, 2 / 3, 0.5)],
cell=[(a, 0, 0),
(-a / 2, a * sqrt(3) / 2, 0),
(0, 0, covera * a)],
pbc=True)
elif crystalstructure == 'diamond':
atoms = bulk(2 * name, 'zincblende', a)
elif crystalstructure == 'zincblende':
s1, s2 = string2symbols(name)
atoms = bulk(s1, 'fcc', a) + bulk(s2, 'fcc', a)
atoms.positions[1] += a / 4
elif crystalstructure == 'rocksalt':
s1, s2 = string2symbols(name)
atoms = bulk(s1, 'fcc', a) + bulk(s2, 'fcc', a)
atoms.positions[1, 0] += a / 2
elif crystalstructure == 'cesiumchloride':
s1, s2 = string2symbols(name)
atoms = bulk(s1, 'sc', a) + bulk(s2, 'sc', a)
atoms.positions[1, :] += a / 2
elif crystalstructure == 'fluorite':
s1, s2, s3 = string2symbols(name)
atoms = bulk(s1, 'fcc', a) + bulk(s2, 'fcc', a) + bulk(s3, 'fcc', a)
atoms.positions[1, :] += a / 4
atoms.positions[2, :] += a * 3 / 4
elif crystalstructure == 'wurtzite':
u = u or 0.25 + 1 / 3 / covera**2
atoms = Atoms(2 * name,
scaled_positions=[(0, 0, 0),
(1 / 3, 2 / 3, 0.5 - u),
(1 / 3, 2 / 3, 0.5),
(0, 0, 1 - u)],
cell=[(a, 0, 0),
(-a / 2, a * sqrt(3) / 2, 0),
(0, 0, a * covera)],
pbc=True)
else:
raise ValueError('Unknown crystal structure: ' + crystalstructure)
return atoms
def rst1(dataset, nlfer, energies):
table1 = ''
nv = 0
for n, l, f, e, rcut in nlfer:
n, l, f = (int(x) for x in [n, l, f])
if n == -1:
n = ''
table1 += ' {}{},{},{:.3f},'.format(n, 'spdf'[l], f, e * Hartree)
if rcut:
table1 += '{:.2f}'.format(rcut)
nv += f
table1 += '\n'
rst = """
{electrons}
====================
Radial cutoffs and eigenvalues:
.. csv-table::
:header: id, occ, eig [eV], cutoff [Bohr]
{table1}
The figure shows convergence of the absolute energy (red line)
and atomization energy (green line) of a {symbol} dimer relative
to completely converged numbers (plane-wave calculation at 1500 eV).
Also shown are finite-difference and LCAO (dzp) calculations at gridspacings
0.143 Å, 0.167 Å and 0.200 Å.
.. image:: {dataset}.png
Egg-box errors in finite-difference mode:
.. csv-table::
:header: grid-spacing [Å], energy error [eV]
{table2}"""
epw, depw, efd, defd, elcao, delcao, deegg = energies
table2 = ''
for h, e in zip([0.16, 0.18, 0.2], deegg):
table2 += ' {:.2f},{:.4f}\n'.format(h, e)
fig = plt.figure(figsize=(8, 5))
ax1 = plt.subplot(121)
ax1.semilogy(cutoffs[:-1], epw[:-1], 'r', label='pw, absolute')
ax1.semilogy(cutoffs[:-1], depw[:-1], 'g', label='pw, atomization')
plt.xticks([200, 400, 600])
plt.xlabel('plane-wave cutoff [eV]')
plt.ylabel('error [eV/atom]')
plt.legend(loc='best')
ax2 = plt.subplot(122, sharey=ax1)
h = [4.0 / g for g in [20, 24, 28]]
ax2.semilogy(h, efd, '-rs', label='fd, absolute')
ax2.semilogy(h, defd, '-gs', label='fd, atomization')
ax2.semilogy(h, elcao, '-ro', label='lcao, absolute')
ax2.semilogy(h, delcao, '-go', label='lcao, atomization')
plt.xticks([0.16, 0.18, 0.2])
plt.xlim(0.14, 0.2)
plt.xlabel(u'grid-spacing [Å]')
plt.legend(loc='best')
plt.setp(ax2.get_yticklabels(), visible=False)
plt.tight_layout()
plt.subplots_adjust(wspace=0)
plt.savefig(dataset + '.png')
plt.close(fig)
return nv, rst.format(electrons=plural(nv, 'valence electron'),
table1=table1,
table2=table2,
symbol=symbol,
dataset=dataset)
def run(opts, args, verbosity):
filename = args.pop(0)
query = ",".join(args)
if query.isdigit():
query = int(query)
add_key_value_pairs = {}
if opts.add_key_value_pairs:
for pair in opts.add_key_value_pairs.split(","):
key, value = pair.split("=")
add_key_value_pairs[key] = convert_str_to_float_or_str(value)
if opts.delete_keys:
delete_keys = opts.delete_keys.split(",")
else:
delete_keys = []
con = connect(filename, use_lock_file=not opts.no_lock_file)
def out(*args):
if verbosity > 0:
print(*args)
if opts.analyse:
con.analyse()
return
if opts.add_from_file:
filename = opts.add_from_file
if ":" in filename:
calculator_name, filename = filename.split(":")
atoms = get_calculator(calculator_name)(filename).get_atoms()
else:
atoms = ase.io.read(filename)
con.write(atoms, key_value_pairs=add_key_value_pairs)
out("Added {0} from {1}".format(atoms.get_chemical_formula(), filename))
return
if opts.count:
n = con.count(query)
print("%s" % plural(n, "row"))
return
if opts.explain:
for dct in con.select(query, explain=True, verbosity=verbosity, limit=opts.limit, offset=opts.offset):
print(dct["explain"])
return
if opts.insert_into:
nkvp = 0
nrows = 0
with connect(opts.insert_into, use_lock_file=not opts.no_lock_file) as con2:
for dct in con.select(query):
kvp = dct.get("key_value_pairs", {})
nkvp -= len(kvp)
kvp.update(add_key_value_pairs)
nkvp += len(kvp)
if opts.unique:
dct["unique_id"] = "%x" % randint(16 ** 31, 16 ** 32 - 1)
con2.write(dct, data=dct.get("data"), **kvp)
nrows += 1
out(
"Added %s (%s updated)"
% (plural(nkvp, "key-value pair"), plural(len(add_key_value_pairs) * nrows - nkvp, "pair"))
)
out("Inserted %s" % plural(nrows, "row"))
return
if add_key_value_pairs or delete_keys:
ids = [dct["id"] for dct in con.select(query)]
m, n = con.update(ids, delete_keys, **add_key_value_pairs)
out(
"Added %s (%s updated)"
% (plural(m, "key-value pair"), plural(len(add_key_value_pairs) * len(ids) - m, "pair"))
)
out("Removed", plural(n, "key-value pair"))
return
if opts.delete:
ids = [dct["id"] for dct in con.select(query)]
if ids and not opts.yes:
msg = "Delete %s? (yes/No): " % plural(len(ids), "row")
if input(msg).lower() != "yes":
return
con.delete(ids)
out("Deleted %s" % plural(len(ids), "row"))
return
if opts.plot:
if ":" in opts.plot:
tags, keys = opts.plot.split(":")
tags = tags.split(",")
else:
tags = []
keys = opts.plot
keys = keys.split(",")
plots = collections.defaultdict(list)
X = {}
labels = []
for row in con.select(query, sort=opts.sort):
name = ",".join(row[tag] for tag in tags)
x = row.get(keys[0])
if x is not None:
if isinstance(x, (unicode, str)):
if x not in X:
X[x] = len(X)
labels.append(x)
x = X[x]
plots[name].append([x] + [row.get(key) for key in keys[1:]])
import matplotlib.pyplot as plt
for name, plot in plots.items():
xyy = zip(*plot)
x = xyy[0]
for y, key in zip(xyy[1:], keys[1:]):
plt.plot(x, y, label=name + key)
if X:
plt.xticks(range(len(labels)), labels, rotation=90)
plt.legend()
plt.show()
return
if opts.long:
dct = con.get(query)
summary = Summary(dct)
summary.write()
elif opts.json:
dct = con.get(query)
con2 = connect(sys.stdout, "json", use_lock_file=False)
kvp = dct.get("key_value_pairs", {})
con2.write(dct, data=dct.get("data"), **kvp)
else:
if opts.open_web_browser:
import ase.db.app as app
app.db = con
app.app.run(host="0.0.0.0", debug=True)
else:
columns = list(all_columns)
c = opts.columns
if c and c.startswith("++"):
keys = set()
for row in con.select(query, limit=opts.limit, offset=opts.offset):
keys.update(row._keys)
columns.extend(keys)
if c[2:3] == ",":
c = c[3:]
else:
c = ""
if c:
if c[0] == "+":
c = c[1:]
elif c[0] != "-":
columns = []
for col in c.split(","):
if col[0] == "-":
columns.remove(col[1:])
else:
columns.append(col.lstrip("+"))
table = Table(con, verbosity, opts.cut)
table.select(query, columns, opts.sort, opts.limit, opts.offset)
if opts.csv:
table.write_csv()
else:
table.write(query)
def run(opts, args, verbosity):
filename = args.pop(0)
query = ','.join(args)
if query.isdigit():
query = int(query)
add_key_value_pairs = {}
if opts.add_key_value_pairs:
for pair in opts.add_key_value_pairs.split(','):
key, value = pair.split('=')
add_key_value_pairs[key] = convert_str_to_float_or_str(value)
if opts.delete_keys:
delete_keys = opts.delete_keys.split(',')
else:
delete_keys = []
con = connect(filename, use_lock_file=not opts.no_lock_file)
def out(*args):
if verbosity > 0:
print(*args)
if opts.analyse:
con.analyse()
return
if opts.add_from_file:
filename = opts.add_from_file
if ':' in filename:
calculator_name, filename = filename.split(':')
atoms = get_calculator(calculator_name)(filename).get_atoms()
else:
atoms = ase.io.read(filename)
con.write(atoms, key_value_pairs=add_key_value_pairs)
out('Added {0} from {1}'.format(atoms.get_chemical_formula(),
filename))
return
if opts.count:
n = con.count(query)
print('%s' % plural(n, 'row'))
return
if opts.explain:
for dct in con.select(query,
explain=True,
verbosity=verbosity,
limit=opts.limit,
offset=opts.offset):
print(dct['explain'])
return
if opts.insert_into:
nkvp = 0
nrows = 0
with connect(opts.insert_into,
use_lock_file=not opts.no_lock_file) as con2:
for dct in con.select(query):
kvp = dct.get('key_value_pairs', {})
nkvp -= len(kvp)
kvp.update(add_key_value_pairs)
nkvp += len(kvp)
if opts.unique:
dct['unique_id'] = '%x' % randint(16**31, 16**32 - 1)
con2.write(dct, data=dct.get('data'), **kvp)
nrows += 1
out('Added %s (%s updated)' %
(plural(nkvp, 'key-value pair'),
plural(len(add_key_value_pairs) * nrows - nkvp, 'pair')))
out('Inserted %s' % plural(nrows, 'row'))
return
if add_key_value_pairs or delete_keys:
ids = [dct['id'] for dct in con.select(query)]
m, n = con.update(ids, delete_keys, **add_key_value_pairs)
out('Added %s (%s updated)' %
(plural(m, 'key-value pair'),
plural(len(add_key_value_pairs) * len(ids) - m, 'pair')))
out('Removed', plural(n, 'key-value pair'))
return
if opts.delete:
ids = [dct['id'] for dct in con.select(query)]
if ids and not opts.yes:
msg = 'Delete %s? (yes/No): ' % plural(len(ids), 'row')
if input(msg).lower() != 'yes':
return
con.delete(ids)
out('Deleted %s' % plural(len(ids), 'row'))
return
if opts.plot_data:
from ase.db.plot import dct2plot
dct2plot(con.get(query).data, opts.plot_data)
return
if opts.plot:
if ':' in opts.plot:
tags, keys = opts.plot.split(':')
tags = tags.split(',')
else:
tags = []
keys = opts.plot
keys = keys.split(',')
plots = collections.defaultdict(list)
X = {}
labels = []
for row in con.select(query, sort=opts.sort):
name = ','.join(str(row[tag]) for tag in tags)
x = row.get(keys[0])
if x is not None:
if isinstance(x, basestring):
if x not in X:
X[x] = len(X)
labels.append(x)
x = X[x]
plots[name].append([x] + [row.get(key) for key in keys[1:]])
import matplotlib.pyplot as plt
for name, plot in plots.items():
xyy = zip(*plot)
x = xyy[0]
for y, key in zip(xyy[1:], keys[1:]):
plt.plot(x, y, label=name + ':' + key)
if X:
plt.xticks(range(len(labels)), labels, rotation=90)
plt.legend()
plt.show()
return
if opts.long:
dct = con.get(query)
summary = Summary(dct)
summary.write()
elif opts.json:
dct = con.get(query)
con2 = connect(sys.stdout, 'json', use_lock_file=False)
kvp = dct.get('key_value_pairs', {})
con2.write(dct, data=dct.get('data'), **kvp)
else:
if opts.open_web_browser:
import ase.db.app as app
app.db = con
app.app.run(host='0.0.0.0', debug=True)
else:
columns = list(all_columns)
c = opts.columns
if c and c.startswith('++'):
keys = set()
for row in con.select(query,
limit=opts.limit,
offset=opts.offset):
keys.update(row._keys)
columns.extend(keys)
if c[2:3] == ',':
c = c[3:]
else:
c = ''
if c:
if c[0] == '+':
c = c[1:]
elif c[0] != '-':
columns = []
for col in c.split(','):
if col[0] == '-':
columns.remove(col[1:])
else:
columns.append(col.lstrip('+'))
table = Table(con, verbosity, opts.cut)
table.select(query, columns, opts.sort, opts.limit, opts.offset)
if opts.csv:
table.write_csv()
else:
table.write(query)
def main(args):
verbosity = 1 - args.quiet + args.verbose
query = ','.join(args.query)
if args.sort.endswith('-'):
args.sort = '-' + args.sort[:-1]
if query.isdigit():
query = int(query)
add_key_value_pairs = {}
if args.add_key_value_pairs:
for pair in args.add_key_value_pairs.split(','):
key, value = pair.split('=')
add_key_value_pairs[key] = convert_str_to_int_float_or_str(value)
if args.delete_keys:
delete_keys = args.delete_keys.split(',')
else:
delete_keys = []
db = connect(args.database, use_lock_file=not args.no_lock_file)
def out(*args):
if verbosity > 0:
print(*args)
if args.analyse:
db.analyse()
return
if args.add_from_file:
filename = args.add_from_file
if ':' in filename:
calculator_name, filename = filename.split(':')
atoms = get_calculator(calculator_name)(filename).get_atoms()
else:
atoms = ase.io.read(filename)
db.write(atoms, key_value_pairs=add_key_value_pairs)
out('Added {0} from {1}'.format(atoms.get_chemical_formula(),
filename))
return
if args.count:
n = db.count(query)
print('%s' % plural(n, 'row'))
return
if args.explain:
for row in db.select(query,
explain=True,
verbosity=verbosity,
limit=args.limit,
offset=args.offset):
print(row['explain'])
return
if args.show_metadata:
print(json.dumps(db.metadata, sort_keys=True, indent=4))
return
if args.set_metadata:
with open(args.set_metadata) as fd:
db.metadata = json.load(fd)
return
if args.insert_into:
nkvp = 0
nrows = 0
with connect(args.insert_into,
use_lock_file=not args.no_lock_file) as db2:
for row in db.select(query, sort=args.sort):
kvp = row.get('key_value_pairs', {})
nkvp -= len(kvp)
kvp.update(add_key_value_pairs)
nkvp += len(kvp)
if args.unique:
row['unique_id'] = '%x' % randint(16**31, 16**32 - 1)
db2.write(row, data=row.get('data'), **kvp)
nrows += 1
out('Added %s (%s updated)' %
(plural(nkvp, 'key-value pair'),
plural(len(add_key_value_pairs) * nrows - nkvp, 'pair')))
out('Inserted %s' % plural(nrows, 'row'))
return
if add_key_value_pairs or delete_keys:
ids = [row['id'] for row in db.select(query)]
m, n = db.update(ids, delete_keys, **add_key_value_pairs)
out('Added %s (%s updated)' %
(plural(m, 'key-value pair'),
plural(len(add_key_value_pairs) * len(ids) - m, 'pair')))
out('Removed', plural(n, 'key-value pair'))
return
if args.delete:
ids = [row['id'] for row in db.select(query)]
if ids and not args.yes:
msg = 'Delete %s? (yes/No): ' % plural(len(ids), 'row')
if input(msg).lower() != 'yes':
return
db.delete(ids)
out('Deleted %s' % plural(len(ids), 'row'))
return
if args.plot_data:
from ase.db.plot import dct2plot
dct2plot(db.get(query).data, args.plot_data)
return
if args.plot:
if ':' in args.plot:
tags, keys = args.plot.split(':')
tags = tags.split(',')
else:
tags = []
keys = args.plot
keys = keys.split(',')
plots = collections.defaultdict(list)
X = {}
labels = []
for row in db.select(query, sort=args.sort, include_data=False):
name = ','.join(str(row[tag]) for tag in tags)
x = row.get(keys[0])
if x is not None:
if isinstance(x, basestring):
if x not in X:
X[x] = len(X)
labels.append(x)
x = X[x]
plots[name].append([x] + [row.get(key) for key in keys[1:]])
import matplotlib.pyplot as plt
for name, plot in plots.items():
xyy = zip(*plot)
x = xyy[0]
for y, key in zip(xyy[1:], keys[1:]):
plt.plot(x, y, label=name + ':' + key)
if X:
plt.xticks(range(len(labels)), labels, rotation=90)
plt.legend()
plt.show()
return
if args.json:
row = db.get(query)
db2 = connect(sys.stdout, 'json', use_lock_file=False)
kvp = row.get('key_value_pairs', {})
db2.write(row, data=row.get('data'), **kvp)
return
db.python = args.metadata_from_python_script
db.meta = process_metadata(db, html=args.open_web_browser)
if args.long:
# Remove .png files so that new ones will be created.
for func, filenames in db.meta.get('functions', []):
for filename in filenames:
try:
os.remove(filename)
except OSError: # Python 3 only: FileNotFoundError
pass
row = db.get(query)
summary = Summary(row, db.meta)
summary.write()
else:
if args.open_web_browser:
import ase.db.app as app
app.databases['default'] = db
app.app.run(host='0.0.0.0', debug=True)
else:
columns = list(all_columns)
c = args.columns
if c and c.startswith('++'):
keys = set()
for row in db.select(query,
limit=args.limit,
offset=args.offset,
include_data=False):
keys.update(row._keys)
columns.extend(keys)
if c[2:3] == ',':
c = c[3:]
else:
c = ''
if c:
if c[0] == '+':
c = c[1:]
elif c[0] != '-':
columns = []
for col in c.split(','):
if col[0] == '-':
columns.remove(col[1:])
else:
columns.append(col.lstrip('+'))
table = Table(db, verbosity, args.cut)
table.select(query, columns, args.sort, args.limit, args.offset)
if args.csv:
table.write_csv()
else:
table.write(query)
def bulk(name,
crystalstructure=None,
a=None,
b=None,
c=None,
*,
alpha=None,
covera=None,
u=None,
orthorhombic=False,
cubic=False,
basis=None):
"""Creating bulk systems.
Crystal structure and lattice constant(s) will be guessed if not
provided.
name: str
Chemical symbol or symbols as in 'MgO' or 'NaCl'.
crystalstructure: str
Must be one of sc, fcc, bcc, hcp, diamond, zincblende,
rocksalt, cesiumchloride, fluorite or wurtzite.
a: float
Lattice constant.
b: float
Lattice constant. If only a and b is given, b will be interpreted
as c instead.
c: float
Lattice constant.
alpha: float
Angle in degrees for rhombohedral lattice.
covera: float
c/a ratio used for hcp. Default is ideal ratio: sqrt(8/3).
u: float
Internal coordinate for Wurtzite structure.
orthorhombic: bool
Construct orthorhombic unit cell instead of primitive cell
which is the default.
cubic: bool
Construct cubic unit cell if possible.
"""
if c is None and b is not None:
# If user passes (a, b) positionally, we want it as (a, c) instead:
c, b = b, c
if covera is not None and c is not None:
raise ValueError("Don't specify both c and c/a!")
xref = None
ref = {}
if name in chemical_symbols:
Z = atomic_numbers[name]
ref = reference_states[Z]
if ref is not None:
xref = ref['symmetry']
# If user did not specify crystal structure, and no basis
# is given, and the reference state says we need one, but
# does not have one, then we can't proceed.
if (crystalstructure is None and basis is None and 'basis' in ref
and ref['basis'] is None):
# XXX This is getting much too complicated, we need to split
# this function up. A lot.
raise RuntimeError('This structure requires an atomic basis')
if ref is None:
ref = {} # easier to 'get' things from empty dictionary than None
if xref == 'cubic':
# P and Mn are listed as 'cubic' but the lattice constants
# are 7 and 9. They must be something other than simple cubic
# then. We used to just return the cubic one but that must
# have been wrong somehow. --askhl
raise RuntimeError('Only simple cubic ("sc") supported')
# Mapping of name to number of atoms in primitive cell.
structures = {
'sc': 1,
'fcc': 1,
'bcc': 1,
'tetragonal': 1,
'bct': 1,
'hcp': 1,
'rhombohedral': 1,
'orthorhombic': 1,
'mcl': 1,
'diamond': 1,
'zincblende': 2,
'rocksalt': 2,
'cesiumchloride': 2,
'fluorite': 3,
'wurtzite': 2
}
if crystalstructure is None:
crystalstructure = xref
if crystalstructure not in structures:
raise ValueError('No suitable reference data for bulk {}.'
' Reference data: {}'.format(name, ref))
if crystalstructure not in structures:
raise ValueError('Unknown structure: {}.'.format(crystalstructure))
# Check name:
natoms = len(string2symbols(name))
natoms0 = structures[crystalstructure]
if natoms != natoms0:
raise ValueError('Please specify {} for {} and not {}'.format(
plural(natoms0, 'atom'), crystalstructure, natoms))
if alpha is None:
alpha = ref.get('alpha')
if a is None:
if xref != crystalstructure:
raise ValueError('You need to specify the lattice constant.')
try:
a = ref['a']
except KeyError:
raise KeyError(
'No reference lattice parameter "a" for "{}"'.format(name))
if b is None:
bovera = ref.get('b/a')
if bovera is not None and a is not None:
b = bovera * a
if crystalstructure in ['hcp', 'wurtzite']:
if cubic:
raise incompatible_cell(want='cubic', have=crystalstructure)
if c is not None:
covera = c / a
elif covera is None:
if xref == crystalstructure:
covera = ref['c/a']
else:
covera = sqrt(8 / 3)
if covera is None:
covera = ref.get('c/a')
if c is None and covera is not None:
c = covera * a
if orthorhombic and crystalstructure not in [
'sc', 'tetragonal', 'orthorhombic'
]:
return _orthorhombic_bulk(name, crystalstructure, a, covera, u)
if cubic and crystalstructure in ['bcc', 'cesiumchloride']:
return _orthorhombic_bulk(name, crystalstructure, a, covera)
if cubic and crystalstructure != 'sc':
return _cubic_bulk(name, crystalstructure, a)
if crystalstructure == 'sc':
atoms = Atoms(name, cell=(a, a, a), pbc=True)
elif crystalstructure == 'fcc':
b = a / 2
atoms = Atoms(name, cell=[(0, b, b), (b, 0, b), (b, b, 0)], pbc=True)
elif crystalstructure == 'bcc':
b = a / 2
atoms = Atoms(name,
cell=[(-b, b, b), (b, -b, b), (b, b, -b)],
pbc=True)
elif crystalstructure == 'hcp':
atoms = Atoms(2 * name,
scaled_positions=[(0, 0, 0), (1 / 3, 2 / 3, 0.5)],
cell=[(a, 0, 0), (-a / 2, a * sqrt(3) / 2, 0),
(0, 0, covera * a)],
pbc=True)
elif crystalstructure == 'diamond':
atoms = bulk(2 * name, 'zincblende', a)
elif crystalstructure == 'zincblende':
s1, s2 = string2symbols(name)
atoms = bulk(s1, 'fcc', a) + bulk(s2, 'fcc', a)
atoms.positions[1] += a / 4
elif crystalstructure == 'rocksalt':
s1, s2 = string2symbols(name)
atoms = bulk(s1, 'fcc', a) + bulk(s2, 'fcc', a)
atoms.positions[1, 0] += a / 2
elif crystalstructure == 'cesiumchloride':
s1, s2 = string2symbols(name)
atoms = bulk(s1, 'sc', a) + bulk(s2, 'sc', a)
atoms.positions[1, :] += a / 2
elif crystalstructure == 'fluorite':
s1, s2, s3 = string2symbols(name)
atoms = bulk(s1, 'fcc', a) + bulk(s2, 'fcc', a) + bulk(s3, 'fcc', a)
atoms.positions[1, :] += a / 4
atoms.positions[2, :] += a * 3 / 4
elif crystalstructure == 'wurtzite':
u = u or 0.25 + 1 / 3 / covera**2
atoms = Atoms(2 * name,
scaled_positions=[(0, 0, 0), (1 / 3, 2 / 3, 0.5 - u),
(1 / 3, 2 / 3, 0.5), (0, 0, 1 - u)],
cell=[(a, 0, 0), (-a / 2, a * sqrt(3) / 2, 0),
(0, 0, a * covera)],
pbc=True)
elif crystalstructure == 'bct':
from ase.lattice import BCT
if basis is None:
basis = ref.get('basis')
if basis is not None:
natoms = len(basis)
lat = BCT(a=a, c=c)
atoms = Atoms([name] * natoms,
cell=lat.tocell(),
pbc=True,
scaled_positions=basis)
elif crystalstructure == 'rhombohedral':
atoms = _build_rhl(name, a, alpha, basis)
elif crystalstructure == 'orthorhombic':
atoms = Atoms(name, cell=[a, b, c], pbc=True)
else:
raise ValueError('Unknown crystal structure: ' + crystalstructure)
if orthorhombic:
assert atoms.cell.orthorhombic
if cubic:
assert abs(atoms.cell.angles() - 90).all() < 1e-10
return atoms
